collaborators

14 papers

cs.LG2026

A Benchmark for Electrical Load Forecasting Across Grid Levels: Time-Series Transformers Outperform Established Methods

Matthias Hertel, Sebastian Pütz, Jonathan Kolar +3

Accurate load forecasting at multiple grid levels is essential for future smart grids, ranging from aggregated control area forecasts for balancing supply and demand to forecasts o…

cs.LG2026

Probabilistic Low-Voltage Peak Load Forecasting with Time Series Foundation Models Evaluated on Application-Oriented Metrics

Benedikt Kaas, Manuel Treutlein, Hannes Benedikt Gerber +5

Low-voltage load forecasting is an important component in current and future energy systems with a high degree of electrification and decentralized generation. However, current for…

cs.LG2026

Explainable Load Forecasting with Covariate-Informed Time Series Foundation Models

Matthias Hertel, Alexandra Nikoltchovska, Sebastian Pütz +3

Time Series Foundation Models (TSFMs) have recently emerged as general-purpose forecasting models and show considerable potential for applications in energy systems. However, appli…

econ.EM2026

Energy-Arena: A Dynamic Benchmark for Operational Energy Forecasting

Max Kleinebrahm, Jonathan Berrisch, Philipp Eiser +11

Energy forecasting research faces a persistent comparability gap that makes it difficult to measure consistent progress over time. Reported accuracy gains are often not directly co…

math.OC2026

Stochastic Model Predictive Control based on Mixed Random Variables for Economic Energy Management

Janik Pinter, Maximilian Beichter, Ralf Mikut +2

Optimal scheduling of batteries has significant potential to reduce electricity costs and to enhance grid resilience. However, effective battery scheduling must account for both ph…

cs.LG2026

Knowledge Distillation for Efficient Transformer-Based Reinforcement Learning in Hardware-Constrained Energy Management Systems

Pascal Henrich, Jonas Sievers, Maximilian Beichter +3

Transformer-based reinforcement learning has emerged as a strong candidate for sequential control in residential energy management. In particular, the Decision Transformer can lear…